NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy
NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy
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DOI：
10.1016/j.neuroimage.2008.08.036
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发表时间：
2009-01-15
期刊：
影响因子：
5.7
通讯作者：
Jang, Jaeduck
中科院分区：
文献类型：
--
作者：
Chul, Jong;Tak, Sungho;Jang, Jaeduck
近红外光谱（NIRS）是一种非侵入性的方法，通过完整颅骨的血红蛋白氧合程度的变化来测量大脑活动。由于光学测量的血红蛋白信号与BOLD信号密切相关，因此使用NIRS和fMRI同时测量可以显著增强时间和空间分辨率。虽然在fMRI中存在强大的统计参数映射工具，但当前用于NIRS的公共领域统计工具在与fMRI同时记录研究的定量分析方面存在一些局限性。本文描述了一种新的公共领域统计工具箱NIRS-SPM。它可以对NIRS信号进行定量分析。更具体地，基于一般线性模型（GLM）和Sun管公式对NIRS数据进行统计分析。p值被计算为表示流形上的非齐次随机场的偏移概率，该表示流形取决于误差协方差矩阵和插值核的结构。NIRS-SPM不仅能够计算氧合血红蛋白、脱氧血红蛋白和总血红蛋白的激活图，而且还允许超分辨率定位，这是使用传统分析工具不可能实现的。使用手指敲击和记忆任务的大量实验结果证实了所提出的方法的可行性。(C)2008年爱思唯尔公司All rights reserved.
Near infrared spectroscopy (NIRS) is a non-invasive method to measure brain activity via changes in the degree of hemoglobin oxygenation through the intact skull. As optically measured hemoglobin signals strongly correlate with BOLD signals, simultaneous measurement using NIRS and fMRI promises a significant mutual enhancement of temporal and spatial resolutions. Although there exists a powerful statistical parametric mapping tool in fMRI, current public domain statistical tools for NIRS have several limitations related to the quantitative analysis of simultaneous recording studies with fMRI. In this paper, a new public domain statistical toolbox known as NIRS-SPM is described. It enables the quantitative analysis of NIRS signal. More specifically, NIRS data are statistically analyzed based on the general linear model (GLM) and Sun's tube formula. The p-values are calculated as the excursion probability of an inhomogeneous random field on a representation manifold that is dependent on the structure of the error covariance matrix and the interpolating kernels. NIRS-SPM not only enables the calculation of activation maps of oxy-, deoxyhemoglobin and total hemoglobin, but also allows for the super-resolution localization, which is not possible using conventional analysis tools. Extensive experimental results using finger tapping and memory tasks confirm the viability of the proposed method. (C) 2008 Elsevier Inc. All rights reserved.